个体对效价预期和风险评估的时程加工机制:来自ERP的证据

采用事件相关电位技术(ERP),通过简单赌博任务,操纵结果预期的效价(输或赢)与风险(高风险与低风险),考察了个体对结果预期阶段的效价和风险评估的脑内时程动态加工过程。脑电结果发现:(1)在N2成分上,结果预期的效价主效应显著,预期输比预期赢能够引起更大的N2波幅;结果预期的风险主效应不显著。(2)在N500成分上,效价与风险之间的交互作用边缘显著,进一步简单效应分析发现,在预期输的条件下,肯定会输的N500波幅显著大于可能会输;而在预期赢的条件下,肯定会赢与可能会赢之间的N500波幅差异不显著。这说明,在不确定决策过程中的结果预期阶段,个体可能先对结果预期的效价进行加工,然后再对风险进行评估。     

论亚当·斯密的宗教市场理论——兼论宗教管制的经济基础

亚当·斯密的宗教市场理论主要包括宗教理性选择理论、宗教自由放任理论和国际宗教管制理论。亚当·斯密的国际宗教管制理论至今仍不失其科学性与合理性。宗教人对来世的追求表明宗教经济人理性选择理论偏离了人的实际;缺乏道德感宗教的客观存在表明宗教并不当然具有道德教化功能;宗教市场的自律调节功能具有有限性,产品市场自身无法克服的诸多弊端同样存在于宗教市场,取消一切宗教管制的宗教自由放任理论不成立,宗教管制因此具有深刻的经济基础,亚当·斯密的宗教市场理论为宗教管制提供了必要的理论基础。     

高职生因果定向与学业表现的关系

为了考察高职生因果定向的特点及因果定向与学生学业表现(学业成绩和专业技能)的关系。该研究以修订的一般因果定向量表为工具,在济南和淄博的三所高职抽取505名学生作为被试进行调查。结果发现:1)修订后的一般因果定向量表具有良好的信效度;2)高职生在因果定向的三个维度上得分差异显著,由高到低依次为自主定向、控制定向和非个人定向;3)自主定向显著正向预测学业表现,控制定向显著负向预测学业表现,而非个人定向对学业表现的预测作用不显著。我国高职生与国外被试的因果定向特点基本一致,说明因果定向具有一定的文化普适性。同时,不同因果定向对学业表现具有不同的预测作用,教育者有必要对学生进行因果定向的教育和引导。     

材料情绪性对条件推理影响的眼动研究

以大学生为被试,采用不同情绪效价的条件命题作为实验材料,运用眼动仪器记录被试进行条件推理的过程和结果,探讨了材料情绪性对条件推理的影响状况.实验采用3(材料情绪性:正性情绪、负性情绪、中性情绪)×4(推理形式:MP、DA、AC、MT)的重复测量实验设计.研究结果发现,正性和负性情绪材料的条件推理成绩显著地低于中性情绪材料,结果支持抑制假说.     

青少年攻击行为与友谊质量的交叉滞后回归分析

该研究采用问卷法,对湖北省一所初级中学二年级青少年进行了一个学年的纵向追踪研究,使用Bjorkquist等人编制的直接和间接攻击量表(DIA)以及Parker和Asher编制的友谊质量问卷(FQQ)测查青少年的身体攻击和友谊质量。然后采用二元交叉滞后回归分析法考察了初中二、三年级学生在一个学年中的攻击行为与友谊质量(总问卷及分问卷得分)的关系。结果表明,在控制了前测"肯定与关心"得分后,身体攻击显著地负向预测了"肯定与关心"得分。在控制了前测的"冲突解决策略"得分后,攻击行为边缘显著地预测了后测的"冲突解决策略"得分。同时发现,后测"身体攻击"和"冲突与背叛"不存在显著的相关。     

宗教与全球发展:一种对话路径

宗教与全球发展成为当代社会的一个重要议题,一个标志在于联合国千禧年发展目标的提出及世界宗教和精神领袖千禧年和平高峰会议。其中,由前世界银行行长及当时的坎特伯雷大主教联合发起的世界信仰发展对话,即为推动类似目标的一个重要机构。发展的目的在于人。贫困不仅包括物质的层面,也包括精神的层面。全球经济的发展需要一种伦理和价值的支撑。文化应该作为一种创新机制,参与并影响全球发展的过程。在中国,宗教是建设社会主义和谐社会的重要因素。中共十七大明确指出,要发挥宗教在社会经济发展中的重要作用。十七届六中全会则明确提出文化体制改革的目标。随着中国改革开放的进一步深入,中国对世界的影响日益加强,文化和精神的维度在社会发展和国家建设方面将扮演更重要的作用。中国模式也将对世界论坛的讨论产生一定影响。     

Active coping with stress suppresses glucose metabolism in the rat hypothalamus

We used 18F-fluorodeoxyglucose small-animal positron-emission tomography to determine whether different styles of coping with stress are associated with different patterns of neuronal activity in the hypothalamus. Adult rats were subjected to immobilization (IMO)-stress or to a non-immobilized condition for 30 min, in random order on separate days, each of which was followed by brain-scanning. Some rats in the immobilized condition were allowed to actively cope with the stress by chewing a wooden stick during IMO, while the other immobilized rats were given nothing to chew on. Voxel-based statistical analysis of the brain imaging data shows that chewing counteracted the stress-induced increased glucose uptake in the hypothalamus to the level of the non-immobilized condition. Region-of-interest analysis of the glucose uptake values further showed that chewing significantly suppressed stress-induced increased glucose uptake in the paraventricular hypothalamic nucleus and the anterior hypothalamic area but not in the lateral hypothalamus. Together with the finding that the mean plasma corticosterone concentration at the termination of the IMO was also significantly suppressed when rats had an opportunity to chew a wooden stick, our results showed that active coping by chewing inhibited the activation of the hypothalamic-pituitary-adrenal axis to reduce the endocrine stress response.     

Selecting a linear mixed model for longitudinal data: repeated measures analysis of variance, covariance pattern model, and growth curve approaches

With increasing popularity, growth curve modeling is more and more often considered as the 1st choice for analyzing longitudinal data. Although the growth curve approach is often a good choice, other modeling strategies may more directly answer questions of interest. It is common to see researchers fit growth curve models without considering alterative modeling strategies. In this article we compare 3 approaches for analyzing longitudinal data: repeated measures analysis of variance, covariance pattern models, and growth curve models. As all are members of the general linear mixed model family, they represent somewhat different assumptions about the way individuals change. These assumptions result in different patterns of covariation among the residuals around the fixed effects. In this article, we first indicate the kinds of data that are appropriately modeled by each and use real data examples to demonstrate possible problems associated with the blanket selection of the growth curve model. We then present a simulation that indicates the utility of Akaike information criterion and Bayesian information criterion in the selection of a proper residual covariance structure. The results cast doubt on the popular practice of automatically using growth curve modeling for longitudinal data without comparing the fit of different models. Finally, we provide some practical advice for assessing mean changes in the presence of correlated data.